Week 2 levels of measurement

Data types & Measurement scales: Data types & Measurement scales Chee-Wee Tan Module P1112
Data is all around us: Data is all around us Collect data everyday. Example: Weather
Where do we get the data?: Where do we get the data? Values
Define the concept /construct: Define the concept /construct Any concept/construct can be conceptually defined. Example: Pain is “an unpleasant sensory & emotional experience associated with actual or potential tissue damage, or described in terms of such damage” (International Association for the Study of Pain, 1994) This is a conceptual definition for ‘pain’
Operational Definition: Operational Definition Abstract construct may require specific definitions to measure it. Example: Pain – ‘pain threshold’ ‘pain intensity’ ‘pain unpleasantness’
Process of measurement: Process of measurement Characteristics of the person/object, not the person/object we’re measuring. Assign of numbers to characteristic.
Measurement scale: Measurement scale The way we assign the numbers and the context will affect the measurement scale. Levels of measurement Properties of the measurement scale
Four measurement scales: Four measurement scales Nominal Ordinal Interval Ratio
Nominal Scale: Nominal Scale ‘In name only’ Labels for identification Mathematical operators cannot be used here. Example: License plate numbers, gender.
Ordinal scale: Ordinal scale Numbers reflect the ordered relationship Takes on features of the nominal scale But since there is order, we can do more in analysis, e.g. finding the median (Week 3) Example: Oxford scale of muscle strength
Interval scale: Interval scale Has all the characteristics of ordinal scale. But allows inferences to be made on the extent of differences. Example: Celsius/Fahrenheit scale (temperature) Arbitrary zero point
Ratio scale: Ratio scale Has all the characteristics of the interval scale. Absolute zero point Or ‘true zero’, or ‘natural zero’. Example: Muscle strength measured in Newtons
Discrete or Continuous variable: Discrete or Continuous variable Discrete Varies in discrete steps Example: number of goals, number of children in a family Continuous Example: Height, weight & time
Types of data: Types of data Data types Modified from Fleming & Nellis (1994), p8.
Why are measurement scales important?: Why are measurement scales important? Progression Transform higher to lower scale. Reverse not generally true.
Why are measurement scales important?: Why are measurement scales important? Metric data (interval & ratio) Use of parametric statistics Non-metric data (nominal & ordinal) Use of non-parametric statistics For this course, only parametric statistics will be mentioned.
Summary: Summary What is a variable & a value? Defining concept/constructs Measurement scales